<p>深入查看代码后,我发现haystack生成的搜索结果是:</p>
<pre><code>{
"query":{
"filtered":{
"filter":{
"fquery":{
"query":{
"query_string":{
"query": "django_ct:(csi.geoname)"
}
},
"_cache":false
}
},
"query":{
"query_string":{
"query": "name_auto:(mid)",
"default_operator":"or",
"default_field":"text",
"auto_generate_phrase_queries":true,
"analyze_wildcard":true
}
}
}
},
"from":0,
"size":6
}
</code></pre>
<p>在elasticsearch中运行这个查询得到的结果是haystack显示的6个对象…但是如果我添加到“query”字符串中</p>
^{pr2}$
<p>一切如愿以偿。所以我们的想法是能够为这个领域设置一个不同的搜索分析器。在</p>
<p>根据@user954994答案的链接和对<a href="https://stackoverflow.com/questions/15923480/elastic-search-search-analyzer-vs-index-analyzer">this post</a>的解释,我最终做的是:</p>
<ol>
<li>我创建了我的自定义elasticsearch后端,在标准的基础上添加了一个新的自定义分析器。在</li>
<li>我添加了一个自定义的EdgeNgramField,允许为索引设置一个特定的分析器(index_analyzer)和另一个用于搜索的分析器(search_analyzer)。在</li>
</ol>
<p>所以,我的新设置是:</p>
<pre><code>ELASTICSEARCH_INDEX_SETTINGS = {
'settings': {
"analysis": {
"analyzer": {
"ngram_analyzer": {
"type": "custom",
"tokenizer": "lowercase",
"filter": ["haystack_ngram"]
},
"edgengram_analyzer": {
"type": "custom",
"tokenizer": "lowercase",
"filter": ["haystack_edgengram"]
},
"suggest_analyzer": {
"type":"custom",
"tokenizer":"standard",
"filter":[
"standard",
"lowercase",
"asciifolding"
]
},
},
"tokenizer": {
"haystack_ngram_tokenizer": {
"type": "nGram",
"min_gram": 3,
"max_gram": 15,
},
"haystack_edgengram_tokenizer": {
"type": "edgeNGram",
"min_gram": 2,
"max_gram": 15,
"side": "front"
}
},
"filter": {
"haystack_ngram": {
"type": "nGram",
"min_gram": 3,
"max_gram": 15
},
"haystack_edgengram": {
"type": "edgeNGram",
"min_gram": 2,
"max_gram": 15
}
}
}
}
}
</code></pre>
<p>我的新的自定义构建架构方法如下所示:</p>
<pre><code>def build_schema(self, fields):
content_field_name, mapping = super(ConfigurableElasticBackend,
self).build_schema(fields)
for field_name, field_class in fields.items():
field_mapping = mapping[field_class.index_fieldname]
index_analyzer = getattr(field_class, 'index_analyzer', None)
search_analyzer = getattr(field_class, 'search_analyzer', None)
field_analyzer = getattr(field_class, 'analyzer', self.DEFAULT_ANALYZER)
if field_mapping['type'] == 'string' and field_class.indexed:
if not hasattr(field_class, 'facet_for') and not field_class.field_type in('ngram', 'edge_ngram'):
field_mapping['analyzer'] = field_analyzer
if index_analyzer and search_analyzer:
field_mapping['index_analyzer'] = index_analyzer
field_mapping['search_analyzer'] = search_analyzer
del(field_mapping['analyzer'])
mapping.update({field_class.index_fieldname: field_mapping})
return (content_field_name, mapping)
</code></pre>
<p>重建索引后,我的映射如下所示:</p>
<pre><code>modelresult: {
_boost: {
name: "boost",
null_value: 1
},
properties: {
django_ct: {
type: "string"
},
django_id: {
type: "string"
},
name_auto: {
type: "string",
store: true,
term_vector: "with_positions_offsets",
index_analyzer: "edgengram_analyzer",
search_analyzer: "suggest_analyzer"
}
}
}
</code></pre>
<p>现在一切如期进行!在</p>
<p><strong>更新:</strong></p>
<p>下面您可以找到代码来澄清这一部分:</p>
<blockquote>
<ol>
<li>I created my custom elasticsearch backend, adding a new custom analyzer based on the standard one.</li>
<li>I added a custom EdgeNgramField, enabling the way to setup an specific analyzer for index (index_analyzer) and another analyzer for
search (search_analyzer).</li>
</ol>
</blockquote>
<p>进入我的应用程序搜索_后端.py公司名称:</p>
<pre><code>from django.conf import settings
from haystack.backends.elasticsearch_backend import ElasticsearchSearchBackend
from haystack.backends.elasticsearch_backend import ElasticsearchSearchEngine
from haystack.fields import EdgeNgramField as BaseEdgeNgramField
# Custom Backend
class CustomElasticBackend(ElasticsearchSearchBackend):
DEFAULT_ANALYZER = None
def __init__(self, connection_alias, **connection_options):
super(CustomElasticBackend, self).__init__(
connection_alias, **connection_options)
user_settings = getattr(settings, 'ELASTICSEARCH_INDEX_SETTINGS', None)
self.DEFAULT_ANALYZER = getattr(settings, 'ELASTICSEARCH_DEFAULT_ANALYZER', "snowball")
if user_settings:
setattr(self, 'DEFAULT_SETTINGS', user_settings)
def build_schema(self, fields):
content_field_name, mapping = super(CustomElasticBackend,
self).build_schema(fields)
for field_name, field_class in fields.items():
field_mapping = mapping[field_class.index_fieldname]
index_analyzer = getattr(field_class, 'index_analyzer', None)
search_analyzer = getattr(field_class, 'search_analyzer', None)
field_analyzer = getattr(field_class, 'analyzer', self.DEFAULT_ANALYZER)
if field_mapping['type'] == 'string' and field_class.indexed:
if not hasattr(field_class, 'facet_for') and not field_class.field_type in('ngram', 'edge_ngram'):
field_mapping['analyzer'] = field_analyzer
if index_analyzer and search_analyzer:
field_mapping['index_analyzer'] = index_analyzer
field_mapping['search_analyzer'] = search_analyzer
del(field_mapping['analyzer'])
mapping.update({field_class.index_fieldname: field_mapping})
return (content_field_name, mapping)
class CustomElasticSearchEngine(ElasticsearchSearchEngine):
backend = CustomElasticBackend
# Custom field
class CustomFieldMixin(object):
def __init__(self, **kwargs):
self.analyzer = kwargs.pop('analyzer', None)
self.index_analyzer = kwargs.pop('index_analyzer', None)
self.search_analyzer = kwargs.pop('search_analyzer', None)
super(CustomFieldMixin, self).__init__(**kwargs)
class CustomEdgeNgramField(CustomFieldMixin, BaseEdgeNgramField):
pass
</code></pre>
<p>我的索引定义如下:</p>
<pre><code>class MyIndex(indexes.SearchIndex, indexes.Indexable):
text = indexes.CharField(document=True, use_template=True)
name_auto = CustomEdgeNgramField(model_attr='name', index_analyzer="edgengram_analyzer", search_analyzer="suggest_analyzer")
</code></pre>
<p>最后,settings当然使用了haystack连接定义的自定义后端:</p>
<pre><code>HAYSTACK_CONNECTIONS = {
'default': {
'ENGINE': 'my_app.search_backends.CustomElasticSearchEngine',
'URL': 'http://localhost:9200',
'INDEX_NAME': 'index'
},
}
</code></pre>